A Step Further in the Understanding of Subjectivity. The Integration Between Interpretative Phenomenological Analysis and Microphenomenological Analysis
Bibliographic record
Abstract
Abstract This chapter presents a pioneering integrative analysis that combines two distinct yet complementary phenomenological methodologies: Interpretative Phenomenological Analysis (IPA) and microphenomenological interviewing. While IPA explores participants articulated personal meanings within a narrative framework, microphenomenology allows for a fine-grained, temporal and sensory dissection of specific lived episodes. Drawing from the philosophical traditions of Husserl, Heidegger, Merleau-Ponty, Ricoeur, Gadamer, and Varela, the chapter proposes a synthesis that captures the complexity of human experience across reflective, prereflective, and linguistic dimensions. Through the comparative mapping of findings from both approaches, a deeper understanding of the subjective flow associated with borderline personality disorder (BPD) is achieved. Five central experiential themes are presented—ranging from thought patterns and interpersonal instability to emotional fluctuations and existential rupture—culminating in a detailed analysis of the unfolding and resolution of emotional instability. The integration emphasizes how these phenomena interweave across identity, behavior, embodiment, and social interaction, providing a layered, multidimensional view of BPD. This novel methodological bridge highlights the value of crossing phenomenological traditions to grasp the richness of complex psychological realities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.028 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".